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| # -------------------------------------------------------- | |
| # BEATs: Audio Pre-Training with Acoustic Tokenizers (https://arxiv.org/abs/2212.09058) | |
| # Github source: https://github.com/microsoft/unilm/tree/master/beats | |
| # Copyright (c) 2022 Microsoft | |
| # Licensed under The MIT License [see LICENSE for details] | |
| # Based on VQGAN code bases | |
| # https://github.com/CompVis/taming-transformers | |
| # --------------------------------------------------------' | |
| import torch | |
| import torch.nn as nn | |
| import torch.nn.functional as F | |
| import torch.distributed as distributed | |
| try: | |
| from einops import rearrange, repeat | |
| except ImportError: | |
| pass | |
| def l2norm(t): | |
| return F.normalize(t, p=2, dim=-1) | |
| def ema_inplace(moving_avg, new, decay): | |
| moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay)) | |
| def sample_vectors(samples, num): | |
| num_samples, device = samples.shape[0], samples.device | |
| if num_samples >= num: | |
| indices = torch.randperm(num_samples, device=device)[:num] | |
| else: | |
| indices = torch.randint(0, num_samples, (num,), device=device) | |
| return samples[indices] | |
| def kmeans(samples, num_clusters, num_iters=10, use_cosine_sim=False): | |
| dim, dtype, device = samples.shape[-1], samples.dtype, samples.device | |
| means = sample_vectors(samples, num_clusters) | |
| for _ in range(num_iters): | |
| if use_cosine_sim: | |
| dists = samples @ means.t() | |
| else: | |
| diffs = rearrange(samples, 'n d -> n () d') \ | |
| - rearrange(means, 'c d -> () c d') | |
| dists = -(diffs ** 2).sum(dim=-1) | |
| buckets = dists.max(dim=-1).indices | |
| bins = torch.bincount(buckets, minlength=num_clusters) | |
| zero_mask = bins == 0 | |
| bins_min_clamped = bins.masked_fill(zero_mask, 1) | |
| new_means = buckets.new_zeros(num_clusters, dim, dtype=dtype) | |
| new_means.scatter_add_(0, repeat(buckets, 'n -> n d', d=dim), samples) | |
| new_means = new_means / bins_min_clamped[..., None] | |
| if use_cosine_sim: | |
| new_means = l2norm(new_means) | |
| means = torch.where(zero_mask[..., None], means, new_means) | |
| return means, bins | |
| class EmbeddingEMA(nn.Module): | |
| def __init__(self, num_tokens, codebook_dim, decay=0.99, eps=1e-5, kmeans_init=True, codebook_init_path=''): | |
| super().__init__() | |
| self.num_tokens = num_tokens | |
| self.codebook_dim = codebook_dim | |
| self.decay = decay | |
| self.eps = eps | |
| if codebook_init_path == '': | |
| if not kmeans_init: | |
| weight = torch.randn(num_tokens, codebook_dim) | |
| weight = l2norm(weight) | |
| else: | |
| weight = torch.zeros(num_tokens, codebook_dim) | |
| self.register_buffer('initted', torch.Tensor([not kmeans_init])) | |
| else: | |
| print(f"load init codebook weight from {codebook_init_path}") | |
| codebook_ckpt_weight = torch.load(codebook_init_path, map_location='cpu') | |
| weight = codebook_ckpt_weight.clone() | |
| self.register_buffer('initted', torch.Tensor([True])) | |
| self.weight = nn.Parameter(weight, requires_grad=False) | |
| self.cluster_size = nn.Parameter(torch.zeros(num_tokens), requires_grad=False) | |
| self.embed_avg = nn.Parameter(weight.clone(), requires_grad=False) | |
| # self.register_buffer('initted', torch.Tensor([not kmeans_init])) | |
| self.update = True | |
| def init_embed_(self, data): | |
| if self.initted: | |
| return | |
| print("Performing Kemans init for codebook") | |
| embed, cluster_size = kmeans(data, self.num_tokens, 10, use_cosine_sim=True) | |
| self.weight.data.copy_(embed) | |
| self.cluster_size.data.copy_(cluster_size) | |
| self.initted.data.copy_(torch.Tensor([True])) | |
| def forward(self, embed_id): | |
| return F.embedding(embed_id, self.weight) | |
| def cluster_size_ema_update(self, new_cluster_size): | |
| self.cluster_size.data.mul_(self.decay).add_(new_cluster_size, alpha=1 - self.decay) | |
| def embed_avg_ema_update(self, new_embed_avg): | |
| self.embed_avg.data.mul_(self.decay).add_(new_embed_avg, alpha=1 - self.decay) | |
| def weight_update(self, num_tokens): | |
| n = self.cluster_size.sum() | |
| smoothed_cluster_size = ( | |
| (self.cluster_size + self.eps) / (n + num_tokens * self.eps) * n | |
| ) | |
| # normalize embedding average with smoothed cluster size | |
| embed_normalized = self.embed_avg / smoothed_cluster_size.unsqueeze(1) | |
| # embed_normalized = l2norm(self.embed_avg / smoothed_cluster_size.unsqueeze(1)) | |
| self.weight.data.copy_(embed_normalized) | |
| def norm_ema_inplace(moving_avg, new, decay): | |
| moving_avg.data.mul_(decay).add_(new, alpha=(1 - decay)) | |
| moving_avg.data.copy_(l2norm(moving_avg.data)) | |
| class NormEMAVectorQuantizer(nn.Module): | |
| def __init__(self, n_embed, embedding_dim, beta, decay=0.99, eps=1e-5, | |
| statistic_code_usage=True, kmeans_init=False, codebook_init_path=''): | |
| super().__init__() | |
| self.codebook_dim = embedding_dim | |
| self.num_tokens = n_embed | |
| self.beta = beta | |
| self.decay = decay | |
| # learnable = True if orthogonal_reg_weight > 0 else False | |
| self.embedding = EmbeddingEMA(self.num_tokens, self.codebook_dim, decay, eps, kmeans_init, codebook_init_path) | |
| self.statistic_code_usage = statistic_code_usage | |
| if statistic_code_usage: | |
| self.register_buffer('cluster_size', torch.zeros(n_embed)) | |
| if distributed.is_available() and distributed.is_initialized(): | |
| print("ddp is enable, so use ddp_reduce to sync the statistic_code_usage for each gpu!") | |
| self.all_reduce_fn = distributed.all_reduce | |
| else: | |
| self.all_reduce_fn = nn.Identity() | |
| def reset_cluster_size(self, device): | |
| if self.statistic_code_usage: | |
| self.register_buffer('cluster_size', torch.zeros(self.num_tokens)) | |
| self.cluster_size = self.cluster_size.to(device) | |
| def forward(self, z): | |
| # reshape z -> (batch, height, width, channel) and flatten | |
| # z, 'b c h w -> b h w c' | |
| # z = rearrange(z, 'b c h w -> b h w c') | |
| # z = z.transpose(1, 2) | |
| z = l2norm(z) | |
| z_flattened = z.reshape(-1, self.codebook_dim) | |
| self.embedding.init_embed_(z_flattened) | |
| d = z_flattened.pow(2).sum(dim=1, keepdim=True) + \ | |
| self.embedding.weight.pow(2).sum(dim=1) - 2 * \ | |
| torch.einsum('bd,nd->bn', z_flattened, self.embedding.weight) # 'n d -> d n' | |
| encoding_indices = torch.argmin(d, dim=1) | |
| z_q = self.embedding(encoding_indices).view(z.shape) | |
| encodings = F.one_hot(encoding_indices, self.num_tokens).type(z.dtype) | |
| if not self.training: | |
| with torch.no_grad(): | |
| cluster_size = encodings.sum(0) | |
| self.all_reduce_fn(cluster_size) | |
| ema_inplace(self.cluster_size, cluster_size, self.decay) | |
| if self.training and self.embedding.update: | |
| # EMA cluster size | |
| bins = encodings.sum(0) | |
| self.all_reduce_fn(bins) | |
| # self.embedding.cluster_size_ema_update(bins) | |
| ema_inplace(self.cluster_size, bins, self.decay) | |
| zero_mask = (bins == 0) | |
| bins = bins.masked_fill(zero_mask, 1.) | |
| embed_sum = z_flattened.t() @ encodings | |
| self.all_reduce_fn(embed_sum) | |
| embed_normalized = (embed_sum / bins.unsqueeze(0)).t() | |
| embed_normalized = l2norm(embed_normalized) | |
| embed_normalized = torch.where(zero_mask[..., None], self.embedding.weight, | |
| embed_normalized) | |
| norm_ema_inplace(self.embedding.weight, embed_normalized, self.decay) | |
| # compute loss for embedding | |
| loss = self.beta * F.mse_loss(z_q.detach(), z) | |
| # preserve gradients | |
| z_q = z + (z_q - z).detach() | |
| # reshape back to match original input shape | |
| # z_q, 'b h w c -> b c h w' | |
| # z_q = rearrange(z_q, 'b h w c -> b c h w') | |
| # z_q = z_q.transpose(1, 2) | |
| return z_q, loss, encoding_indices |